A New Ensemble Diversity Measure Applied to Thinning Ensembles
Lecture notes in computer sciencePublished 1 January 2003
Robert E. Banfield, Lawrence Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Citations86
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
A new way of describing the diversity of an ensemble of classifiers, the Percentage Correct Diversity Measure, is introduced and it is shown that diversity is generally modeled by the measure and ensembles can be made smaller without loss in accuracy.
Abstract
We introduce a new way of describing the diversity of an ensemble of classifiers, the Percentage Correct Diversity Measure, and compare it against existing methods. We then introduce two new methods for removing classifiers from an ensemble based on diversity calculations. Empirical results for twelve datasets from the UC Irvine repository show that diversity is generally modeled by our measure and ensembles can be made smaller without loss in accuracy.
Keywords
Computer Science
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